Top Data & Analytics Jobs

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

Leads development, validation, implementation, and monitoring of credit scorecards, loss forecasting, collection, recovery, and pricing models for auto lending. Provides strategic recommendations balancing growth, profitability, risk, and regulatory compliance. Partners with Pricing, Risk, Finance, and senior leadership, translating complex statistical analyses into actionable business insights. Uses large-scale data and advanced analytics to monitor portfolio performance, identify risks, and improve forecasting accuracy.

Build dashboards, automate reporting and reconciliation workflows, apply AI to business processes, and develop data-driven analysis for Credit Trading COO leadership. The role partners with Trading, Sales, Technology, Operations, Risk, and Finance to improve processes, standardize reporting, and translate business questions into scalable analytics solutions.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Designs and delivers geospatial analytics solutions for healthcare business problems. Responsibilities include advanced geospatial analysis, geographic data asset development through ETL and automation, GIS platform enhancements and maintenance, solution design, internal customer engagement, marketing collateral, and knowledge sharing. The role also explores AI and GeoAI applications, supports enterprise geospatial technology patterns, and partners with business units on analytics projects.

Develop, implement, monitor, and enhance credit risk models using statistical, econometric, and machine learning methods. Analyze large datasets with Python, SQL, SAS, R, and Spark; perform forecasting, stress testing, feature engineering, and model validation. Document methodologies, support governance and regulatory reviews, communicate findings to stakeholders, and improve analytical automation and reporting processes.

Develop, implement, monitor, and enhance credit risk models using statistical, econometric, and machine learning methods. Analyze large datasets with Python, SQL, SAS, R, and Spark to support risk measurement, forecasting, stress testing, and portfolio management. Document methodologies, support validation and audits, address regulatory findings, and communicate model results to technical and executive stakeholders while following model governance requirements.

Analyze customer behavior across digital and phone journeys, develop experiments and OKRs, and translate data into actionable recommendations. Partner with product, technology, design, modeling, and analytics teams to improve customer experiences and operational efficiency. Present insights to senior stakeholders, support predictive analytics and machine learning initiatives, and maintain data governance through collaboration with data owners.

Leads enterprise healthcare data engineering strategy, cloud data platform modernization, warehouse and lakehouse architecture, ETL/ELT pipelines, streaming, interoperability, governance, data quality, and master data management. Manages data engineering, analytics, and platform teams while supporting AI/ML initiatives. Requires extensive healthcare data experience, leadership of large programs, cloud and data engineering expertise, knowledge of claims and clinical systems, and familiarity with HL7, FHIR, EHR platforms, and healthcare compliance.

Performs data analysis, validation, mining, processing, modeling, and ETL activities on large, diverse datasets. Collaborates with senior team members to build backend data infrastructure, assess data accuracy, identify inconsistencies, and deliver business insights through reporting and visualization. The role uses SQL, Python, machine learning, cloud technologies, data governance, and data quality practices to support digital product development and data-driven decisions.

Designs and enhances fraud strategies using data analysis, advanced analytics, and AI/machine learning. Monitors fraud trends and customer friction, identifies root causes, and develops solutions that balance fraud mitigation, financial performance, customer experience, and operational efficiency. Partners with Product, Technology, and Operations to define requirements, implement capabilities, and validate results. Requires strategic planning, roadmap development, project management, compliance collaboration, and strong stakeholder communication.

Build predictive models forecasting customer behavior, analyze retention and gameplay trends, and create automated tools, analyses, and dashboards. Translate model outputs into actionable recommendations that guide marketing investments, promotional strategies, customer valuation, and retention. Partner with Marketing, Finance, Data Science, and Data Engineering teams while using Databricks, Airflow, Bitbucket, SQL, Snowflake, and dashboarding tools to support revenue growth and customer insights.

Build predictive models and customer economics analyses that forecast behavior, optimize marketing investments, and support revenue growth. Develop automated tools, dashboards, and repeatable frameworks to communicate customer performance and retention trends. Partner with Marketing, Finance, Data Science, and Data Engineering to translate complex model outputs into actionable business strategies using technologies including Databricks, Airflow, Bitbucket, SQL, and Snowflake.

Manage point-of-sale fraud risk strategies, analyze complex data, identify fraud trends and risk indicators, develop reporting and detection tools, and recommend mitigation strategies. The role collaborates with Operations, IT, and Risk Management teams, communicates findings to senior leaders, and helps implement and test fraud policies, rules, cutoffs, and operational processes while balancing loss reduction with customer experience.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design secure, resilient, and operationally efficient data systems, including streaming and AI-enabled solutions. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.

Leads the technical vision, architecture, and delivery of enterprise search analytics and ontology solutions for ecommerce. Manages and mentors software engineering teams, integrates platforms, oversees IT service operations and quality, and improves search relevance using NLP, vector search, personalization, machine learning, and performance KPIs. Partners with product, data science, and engineering leaders while driving ontology, metadata, governance, and semantic search adoption across digital commerce.

Leads business intelligence and analytics initiatives for Caterpillar’s components organizations. Partners with stakeholders to identify opportunities, gather requirements, prioritize roadmaps, and deliver Power BI, Sigma, Snowflake, web application, automation, and AI solutions. Oversees portfolio governance, communications, risk management, milestone tracking, process improvement, and adoption while translating business needs into scalable, data-driven solutions across engineering, supply chain, manufacturing, commercial, and product teams.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Build and maintain cloud data pipelines, analytics platforms, data models, and AI-powered solutions using Azure, Airflow, Databricks, Snowflake, and Power BI. Secure cloud environments through Azure networking, PIM, RBAC, and vulnerability remediation. Collaborate with infrastructure and delivery teams on deployment, monitoring, automation, responsible AI adoption, and continuous platform improvement.

Build data pipelines and relational data models using Python and SQL; clean operational data, support scenario analysis, and create dashboards for PMO leadership. Explore LLMs and machine learning for text analysis and data-quality automation. Gain production experience with testing, documentation, maintainable code, and data-driven decision support in a trading environment.

Leads enterprise sanctions-screening analytics strategy, developing KPI frameworks, governance standards, dashboards, root-cause analyses, and predictive insights. Identifies false-positive drivers, workflow bottlenecks, and automation opportunities while recommending tuning and process improvements. Partners with technology, operations, compliance, and governance stakeholders to build business cases, implement analytics-enabled automation, monitor performance, and deliver executive-ready reporting.

Develops fraud rules, models, dashboards, and strategies to reduce fraud losses. Analyzes emerging fraud trends, monitors KPIs, conducts ad hoc analysis, supports production deployments, and communicates findings to technical and business stakeholders. The Thursday-through-Monday schedule requires weekend availability, with a hybrid arrangement based in Chicago.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.